
Hailiang ZhaoZhejiang University | ZJU · College of Computer Science and Technology
Hailiang Zhao
PhD student
About
28
Publications
10,258
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904
Citations
Citations since 2017
Introduction
Currently I am a fourth-year Ph.D. student of College of Computer Science and Technology, Zhejiang University. Before my Ph.D. career, I was an undergraduate student from Wuhan University of Technology and received my B.Eng. degree in Computer Science and Technology on June, 2019. In September 2019, I was admitted to study for a Ph.D. degree in Zhejiang University under the supervision of Prof. Shuiguang Deng without entrance examination. My homepage is http://hliangzhao.me/
Publications
Publications (28)
Collaboration spaces formed from edge servers can efficiently improve the quality of experience of service subscribers. In this paper, we first utilize a strategy based on the density of Internet of Things (IoT) devices and
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-means algorithm to partition network of edge servers, then an algorithm for IoT devices’ computation offloading decisi...
Mobile Edge Computing (MEC) has already developed into a key component of the future mobile broadband network due to its low latency. In MEC, mobile devices can access data-intensive applications deployed at edge, which are facilitated by service and computing resources available on edge servers. However, it is difficult to handle such issues while...
Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of co...
Along with the deepening development in communication technologies and the surge of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the upgrade of hardware architectures. Billions of bytes of data...
Multi-access Edge Computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud to the network edge to provide services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming back...
Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional heavy workload bursts. This makes workload prediction challenging. There are mainly two categories of workload prediction methods: statistical methods and neural-network-based ones. The former ones rely o...
p>The development of cloud computing delivery mod- els inspires the emergence of cloud-native computing. Cloud- native computing, as the most influential development principle for web applications, has already attracted increasingly more at- tention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clea...
p>The development of cloud computing delivery mod- els inspires the emergence of cloud-native computing. Cloud- native computing, as the most influential development principle for web applications, has already attracted increasingly more at- tention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clea...
Nowadays, multi-server jobs, which request multiple computing devices and hold onto them during their execution, dominate modern computing clusters. When allocating computing devices to them, it is difficult to make the tradeoff between the parallel computation gains and the internal communication overheads. Firstly, the computing gain does not inc...
Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal comm...
Multi-server jobs are imperative in modern cloud computing systems. A multi-server job has multiple components and requests multiple servers for being served. How to allocate restricted computing devices to jobs is a topic of great concern, which leads to the job scheduling and load balancing algorithms thriving. However, current job dispatching al...
In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offload tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to...
Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. Firstly, the scheduling decisions have to satis...
Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application...
Serverless computing is leading the way to a simplified and general purpose programming model for the cloud. A key enabler behind serverless is efficient load balancing, which routes continuous workloads to appropriate backend resources. However, current load balancing algorithms implemented in Kubernetes native serverless platforms are simple heur...
Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network...
Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network...
Edge computing is naturally suited to the applications generated by Internet of Things (IoT) nodes. The IoT applications generally take the form of directed acyclic graphs (DAGs), where vertices represent interdependent functions and edges represent data streams. The status quo of minimizing the makespan of the DAG motivates the study on optimal fu...
Multi-access Edge Computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud to the network edge to provide services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming back...
Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then,...
Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billion...
An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly ad...
Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billion...